Guignet Michelle, Schmuck Martin, Harvey Danielle J, Nguyen Danh, Bruun Donald, Echeverri Angela, Gurkoff Gene, Lein Pamela J
Department of Molecular Biosciences, School of Veterinary Medicine, University of California-Davis, 1089 Veterinary Medicine Drive, Davis, CA, 95616, USA.
Department of Public Health Sciences, University of California-Davis, One Shields Avenue, Davis, CA, 95616, USA.
Heliyon. 2023 Feb 8;9(2):e13449. doi: 10.1016/j.heliyon.2023.e13449. eCollection 2023 Feb.
The field of cell biology has seen major advances in both cellular imaging modalities and the development of automated image analysis platforms that increase rigor, reproducibility, and throughput for large imaging data sets. However, there remains a need for tools that provide accurate morphometric analysis of single cells with complex, dynamic cytoarchitecture in a high-throughput and unbiased manner. We developed a fully automated image-analysis algorithm to rapidly detect and quantify changes in cellular morphology using microglia cells, an innate immune cell within the central nervous system, as representative of cells that exhibit dynamic and complex cytoarchitectural changes. We used two preclinical animal models that exhibit robust changes in microglia morphology: (1) a rat model of acute organophosphate intoxication, which was used to generate fluorescently labeled images for algorithm development; and (2) a rat model of traumatic brain injury, which was used to validate the algorithm using cells labeled using chromogenic detection methods. All brain sections were immunolabeled for IBA-1 using fluorescence or diaminobenzidine (DAB) labeling, images were acquired using a high content imaging system and analyzed using a custom-built algorithm. The exploratory data set revealed eight statistically significant and quantitative morphometric parameters that distinguished between phenotypically distinct groups of microglia. Manual validation of single-cell morphology was strongly correlated with the automated analysis and was further supported by a comparison with traditional stereology methods. Existing image analysis pipelines rely on high-resolution images of individual cells, which limits sample size and is subject to selection bias. However, our fully automated method integrates quantification of morphology and fluorescent/chromogenic signals in images from multiple brain regions acquired using high-content imaging. In summary, our free, customizable image analysis tool provides a high-throughput, unbiased method for accurately detecting and quantifying morphological changes in cells with complex morphologies.
细胞生物学领域在细胞成像技术以及自动图像分析平台的开发方面都取得了重大进展，这些平台提高了大型成像数据集的严谨性、可重复性和通量。然而，仍然需要一些工具，能够以高通量且无偏差的方式对具有复杂动态细胞结构的单个细胞进行准确的形态计量分析。我们开发了一种全自动图像分析算法，以小胶质细胞（中枢神经系统中的一种固有免疫细胞）为代表，快速检测和量化细胞形态的变化，小胶质细胞展现出动态且复杂的细胞结构变化。我们使用了两种在小胶质细胞形态上有显著变化的临床前动物模型：（1）急性有机磷中毒大鼠模型，用于生成荧光标记图像以开发算法；（2）创伤性脑损伤大鼠模型，用于使用显色检测方法标记的细胞来验证算法。所有脑切片均使用荧光或二氨基联苯胺（DAB）标记对IBA - 1进行免疫标记，使用高内涵成像系统采集图像，并使用定制算法进行分析。探索性数据集揭示了八个具有统计学意义的定量形态计量参数，可区分表型不同的小胶质细胞组。单细胞形态的手动验证与自动分析高度相关，并通过与传统体视学方法的比较得到进一步支持。现有的图像分析流程依赖于单个细胞的高分辨率图像，这限制了样本量并存在选择偏差。然而，我们的全自动方法整合了使用高内涵成像采集的来自多个脑区图像中的形态和荧光/显色信号的量化。总之，我们免费的、可定制的图像分析工具提供了一种高通量、无偏差的方法，用于准确检测和量化具有复杂形态的细胞中的形态变化。
